Multiple-Sensor Planning and Information Integration for Automatic Coordinate Metrology

2001 ◽  
Vol 1 (2) ◽  
pp. 167-179 ◽  
Author(s):  
Tzung-Sz Shen ◽  
Jianbing Huang ◽  
Chia-Hsiang Menq
Keyword(s):  
High Precision ◽  
Information Integration ◽  
Vision System ◽  
Sensor Integration ◽  
Sensor Planning ◽  
Multiple Sensors ◽  
Surface Areas ◽  
Coordinate Metrology ◽  
Key Issues ◽  
Multiple Sensor

Multiple-sensor integration of vision and touch probe sensors has been shown to be a feasible approach for rapid and high-precision coordinate acquisition [Shen, T. S., Huang, J., and Meng, C. H., 2000, “Multiple-sensor integration for rapid and high-precision coordinate metrology,” IEEE/ASME Trans. Mechatron. 5, pp. 110–121]. However, the automation of coordinate measurements is still hindered by unknown surface areas that cannot be digitized using the vision system due to occlusions. It is identified that the estimation and reasoning of unknown surface areas, and automatic sensor planning using multiple sensors are two key issues. In order to advance multiple-sensor integration technologies toward a fully automatic and agile coordinate metrology, information integration algorithms for estimating and reasoning unknown surface areas, and an automatic multiple-sensor planning environment are developed in this paper. Experimental and simulation results are also demonstrated.

Multiple-sensor integration for rapid and high-precision coordinate metrology

2000 ◽  
Vol 5 (2) ◽  
pp. 110-121 ◽  
Author(s):  
Tzung-Sz Shen ◽  
Jianbing Huang ◽  
Chia-Hsiang Menq
Keyword(s):  
High Precision ◽  
Sensor Integration ◽  
Coordinate Metrology ◽  
Multiple Sensor

Inference Degradation in Information Fusion

2010 ◽  
pp. 92-109
Author(s):  
Xiangyang Li
Keyword(s):  
Information Fusion ◽  
Information Integration ◽  
Dynamic Bayesian Network ◽  
Recognition Task ◽  
Selection Strategy ◽  
Optimization Approach ◽  
Sensor Planning ◽  
Multiple Sensors ◽  
Information Awareness ◽  
Security Surveillance

Dynamic and active information fusion processes select the best sensor based on expected utility calculation in order to integrate the evidences acquires both accurately and timely. However, inference degradation happens when the same/similar sensors are selected repeatedly over time if the selection strategy is not well designed that considers the history of sensor engagement. This phenomenon decreases fusion accuracy and efficiency, in direct conflict to the objective of information integration with multiple sensors. This chapter tries to provide a mathematical scrutiny of this problem in the myopia planning popularly utilized in active information fusion. In evaluation it first introduces the common active information fusion context using security surveillance applications. It then examines the generic dynamic Bayesian network model for a mental state recognition task and analyzes experimentation results for the inference degradation. It also discusses the candidate solutions with some preliminary results. The inference degradation problem is not limited to the discussed task and may emerge in variants of sensor planning strategies even with more global optimization approach. This study provides common guidelines in information integration applications for information awareness and intelligent decision.

Robust and High-precision Vision System for Deflection Measurement of Crane Girder with Camera Shake Reduction

2020 ◽  
pp. 1-1
Author(s):  
Xiao Zhou ◽  
Jinju Wang ◽  
Xingang Mou ◽  
Xincheng Li ◽  
Liuhui Xie ◽  
...  
Keyword(s):  
High Precision ◽  
Vision System ◽  
Deflection Measurement ◽  
Camera Shake

Vehicle Security Distance Control Based on Binocular Parallax Vision

2014 ◽  
Vol 644-650 ◽  
pp. 207-210
Author(s):  
Shuang Liu ◽  
Xiang Jie Kong ◽  
Ming Cai Shan
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
High Precision ◽  
Stereo Matching ◽  
Vision System ◽  
Intelligent Transportation ◽  
Distance Control ◽  
Calibration Methods ◽  
Computer Vision Technology ◽  
Vehicle Security

Binocular parallax vision system is a kind of computer vision technology. Two cameras on different locations can get two different pictures of same object. The space position of the object can be calculated by the parallax information of two different pictures. The binocular parallax vision technology includes cameras calibration, image processing, and stereo matching analysis. The paper will introduce the inside and outside parameters calibration methods, and combing the traffic applications, designed the calibrating scheme. The parameters that obtained according to the scheme can meet the demands of measuring the vehicle distance. The high precision can meet the needs of intelligent transportation vehicles in a security vehicles spacing survey, which is an effective way for measuring the front car distance.

scholarly journals Badminton motion capture with visual image detection of picking robotics

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096907
Author(s):  
Changxin Li
Keyword(s):  
Image Processing ◽  
Control System ◽  
Motion Capture ◽  
High Precision ◽  
Target Recognition ◽  
Vision System ◽  
Recognition Rate ◽  
Motion Capture System ◽  
Vision Control ◽  
Edge Features

In the process of strawberry easily broken fruit picking, in order to reduce the damage rate of the fruit, improves accuracy and efficiency of picking robot, field put forward a motion capture system based on international standard badminton edge feature detection and capture automation algorithm process of night picking robot badminton motion capture techniques training methods. The badminton motion capture system can analyze the game video in real time and obtain the accuracy rate of excellent badminton players and the technical characteristics of badminton motion capture through motion capture. The purpose of this article is to apply the high-precision motion capture vision control system to the design of the vision control system of the robot in the night picking process, so as to effectively improve the observation and recognition accuracy of the robot in the night picking process, so as to improve the degree of automation of the operation. This paper tests the reliability of the picking robot vision system. Taking the environment of picking at night as an example, image processing was performed on the edge features of the fruits picked by the picking robot. The results show that smooth and enhanced image processing can successfully extract edge features of fruit images. The accuracy of the target recognition rate and the positioning ability of the vision system of the picking robot were tested by the edge feature test. The results showed that the accuracy of the target recognition rate and the positioning ability of the motion edge of the vision system were far higher than 91%, satisfying the automation demand of the picking robot operation with high precision.

scholarly journals Flexible method for accurate calibration of large-scale vision metrology system based on virtual 3-D targets and laser tracker

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989351
Author(s):  
Xi Zhang ◽  
Yuanzhi Xu ◽  
Haichao Li ◽  
Lijing Zhu ◽  
Xin Wang ◽  
...  
Keyword(s):  
High Precision ◽  
Stereo Vision ◽  
Large Scale ◽  
Vision System ◽  
Calibration Method ◽  
Field Of View ◽  
Large Field ◽  
Laser Tracker ◽  
Calibration Target ◽  
Pose Tracking

For the purpose of obtaining high-precision in stereo vision calibration, a large-size precise calibration target, which can cover more than half of the field of view is vital. However, large-scale calibration targets are very difficult to fabricate. Based on the idea of error tracing, a high-precision calibration method for vision system with large field of view by constructing a virtual 3-D calibration target with a laser tracker was proposed in this article. A virtual 3-D calibration target that covers the whole measurement space can be established flexibly and the measurement precision of the vision system can be traceable to the laser tracker. First, virtual 3-D targets by calculating rigid body transformation with unit quaternion method were constructed. Then, the high-order distortion camera model was taken into consideration. Besides, the calibration parameters were solved with Levenberg–Marquardt optimization algorithm. In the experiment, a binocular stereo vision system with the field of view of 4 × 3 × 2 m3 was built for verifying the validity and precision of the proposed calibration method. It is measured that the accuracy with the proposed method can be greatly improved comparing with traditional plane calibration method. The method can be widely used in industrial applications, such as in the field of calibrating large-scale vision-based coordinate metrology, and six-degrees of freedom pose tracking system for dimensional measurement of workpiece, as well as robotics geometrical accuracy detection and compensation.

scholarly journals Oil seal surface defect detection using superpixel segmentation and circumferential difference

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097651
Author(s):  
Zhendong He ◽  
Jie Liu ◽  
Liying Jiang ◽  
Suna Zhao ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Vision System ◽  
Small Variation ◽  
Low Contrast ◽  
Superpixel Segmentation ◽  
Surface Image ◽  
Surface Defect Detection ◽  
Key Issues ◽  
Background Difference

Surface defects affect the quality and safety of oil seals. It is a challenge to detect such defects in a vision system because of the unequal reflection property of oil seal surfaces and low contrast between the defect and the background. This article proposes a visual detection method (VDM) for oil seal surface defects and outlines two key issues of VDMs. First, we present a superpixel segmentation algorithm based on the significant gray level variation in the radial direction of an oil seal surface image. This image is then divided into several ring belts. Subsequently, considering the reflection inequality and low contrast, we propose a new circumferential background difference algorithm based on the small variation along the circumferential direction of the image. This algorithm eliminates the influence of the reflection inequality and improves the contrast distinction between the defects and the background. The experimental results verify the effectiveness of the proposed method with a recall and precision as high as 95.2% and 86.8%, respectively.

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